{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) | [Contents](Index.ipynb) | [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb) >\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.08-Random-Forests.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# In-Depth: Decision Trees and Random Forests\n",
    "\n",
    "# 深入：决策树和随机森林"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Previously we have looked in depth at a simple generative classifier (naive Bayes; see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb)) and a powerful discriminative classifier (support vector machines; see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)).\n",
    "Here we'll take a look at motivating another powerful algorithm—a non-parametric algorithm called *random forests*.\n",
    "Random forests are an example of an *ensemble* method, meaning that it relies on aggregating the results of an ensemble of simpler estimators.\n",
    "The somewhat surprising result with such ensemble methods is that the sum can be greater than the parts: that is, a majority vote among a number of estimators can end up being better than any of the individual estimators doing the voting!\n",
    "We will see examples of this in the following sections.\n",
    "We begin with the standard imports:\n",
    "\n",
    "前面我们深入的介绍了简单的生成分类器（朴素贝叶斯，参见[深入：朴素贝叶斯分类](05.05-Naive-Bayes.ipynb)）和强大的决定分类器（支持向量机，参见[深入：支持向量机](05.07-Support-Vector-Machines.ipynb)）。下面我来看另外一种强大的算法，一个没有参数被称为*随机森林*的算法。随机森林是一个集成的方法，这意味着它是建立在一系列简单评估器的组合基础上的。令人惊奇的是，这种方法的组合会比独立的算法应用要强大：即通过多个评估器得到的选举多数票结果要由于其中任何一个独立的评估器得到的结果。我们会在本节后面看到一些例子。首先还是进行需要的包导入："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns; sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Motivating Random Forests: Decision Trees\n",
    "\n",
    "## 开始学习随机森林：决策树"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Random forests are an example of an *ensemble learner* built on decision trees.\n",
    "For this reason we'll start by discussing decision trees themselves.\n",
    "\n",
    "随机森林是构建在决策树基础上的*组合学习*的一种方法。因此我们先讨论一下决策树本身。\n",
    "\n",
    "> Decision trees are extremely intuitive ways to classify or label objects: you simply ask a series of questions designed to zero-in on the classification.\n",
    "For example, if you wanted to build a decision tree to classify an animal you come across while on a hike, you might construct the one shown here:\n",
    "\n",
    "决策树是用来分类或者标记对象的非常直观的方法：你只需要简单的提出一系列设计好的问题，最终达到分类标签即可。例如，如果希望构建一个用来分类动物的决策树，你可以构建下面的这棵树："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](figures/05.08-decision-tree.png)\n",
    "[附录中生成图像的代码](06.00-Figure-Code.ipynb#Decision-Tree-Example)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The binary splitting makes this extremely efficient: in a well-constructed tree, each question will cut the number of options by approximately half, very quickly narrowing the options even among a large number of classes.\n",
    "The trick, of course, comes in deciding which questions to ask at each step.\n",
    "In machine learning implementations of decision trees, the questions generally take the form of axis-aligned splits in the data: that is, each node in the tree splits the data into two groups using a cutoff value within one of the features.\n",
    "Let's now look at an example of this.\n",
    "\n",
    "这种二元的区分方式使得算法非常高效：在一个构造良好的树中，每个问题都会使得剩下的可用选项减半，这甚至在分类数量很多情况下也能迅速的得到结果。当然这个效率取决于每一步设计问题的技巧。在决策树的机器学习实现中，树中的问题通常都采用沿着轴来分割数据：也就是说，树中的每个节点会在数据的一个特征上应用一个阈值来将数据一分为二。下面我们看一个例子。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a decision tree\n",
    "\n",
    "### 创建决策树\n",
    "\n",
    "> Consider the following two-dimensional data, which has one of four class labels:\n",
    "\n",
    "考虑下面的二维数据，具有四个分类标签："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "\n",
    "X, y = make_blobs(n_samples=300, centers=4,\n",
    "                  random_state=0, cluster_std=1.0)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='rainbow');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> A simple decision tree built on this data will iteratively split the data along one or the other axis according to some quantitative criterion, and at each level assign the label of the new region according to a majority vote of points within it.\n",
    "This figure presents a visualization of the first four levels of a decision tree classifier for this data:\n",
    "\n",
    "在这个数据上建立的简单决策树会沿着两个轴来分类数据，每一层的划分都会按照区域中大多数数据点的分类标签（多数票）来确定区域的标签值。下面的图像展示了头四层的决策树进行分类的可视化过程："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](figures/05.08-decision-tree-levels.png)\n",
    "[附录中生成图像的代码](06.00-Figure-Code.ipynb#Decision-Tree-Levels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Notice that after the first split, every point in the upper branch remains unchanged, so there is no need to further subdivide this branch.\n",
    "Except for nodes that contain all of one color, at each level *every* region is again split along one of the two features.\n",
    "\n",
    "上图看到第一层分类后，图中上部的分支一直保持不变，因此没有必要再对这个分支进行细分了。除非某个节点已经达到包含同一颜色的目的，否则每一层的不同区域都是再次沿着两个特征其中之一对数据进行再次细分。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> This process of fitting a decision tree to our data can be done in Scikit-Learn with the ``DecisionTreeClassifier`` estimator:\n",
    "\n",
    "这个决策树的拟合过程可以通过Scikit-Learn中的`DecisionTreeClassifier`评估器来实现："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "tree = DecisionTreeClassifier().fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Let's write a quick utility function to help us visualize the output of the classifier:\n",
    "\n",
    "然后我们写一个工具函数帮助我们展示分类器的数据可视化："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_classifier(model, X, y, ax=None, cmap='rainbow'):\n",
    "    ax = ax or plt.gca()\n",
    "    \n",
    "    # 绘制训练集数据点\n",
    "    ax.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=cmap,\n",
    "               clim=(y.min(), y.max()), zorder=3)\n",
    "    ax.axis('tight')\n",
    "    ax.axis('off')\n",
    "    xlim = ax.get_xlim()\n",
    "    ylim = ax.get_ylim()\n",
    "    \n",
    "    # 模型拟合\n",
    "    model.fit(X, y)\n",
    "    xx, yy = np.meshgrid(np.linspace(*xlim, num=200),\n",
    "                         np.linspace(*ylim, num=200))\n",
    "    Z = model.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n",
    "\n",
    "    # 填充结果区域\n",
    "    n_classes = len(np.unique(y))\n",
    "    contours = ax.contourf(xx, yy, Z, alpha=0.3,\n",
    "                           levels=np.arange(n_classes + 1) - 0.5,\n",
    "                           cmap=cmap, zorder=1)\n",
    "\n",
    "    ax.set(xlim=xlim, ylim=ylim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Now we can examine what the decision tree classification looks like:\n",
    "\n",
    "下面我们可以看一下决策树分类器的分类效果了："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_classifier(DecisionTreeClassifier(), X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> If you're running this notebook live, you can use the helpers script included in [The Online Appendix](06.00-Figure-Code.ipynb#Helper-Code) to bring up an interactive visualization of the decision tree building process:\n",
    "\n",
    "如果你在使用交互式的notebook的话，你可以使用使用一个工具脚本[附录中工具脚本](06.00-Figure-Code.ipynb#Helper-Code)来展示决策树动态可视化构建过程："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e76dca9c5b0345d8b0717062dc5acbcd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='depth', index=1, options=(1, 5), value=5), Output()), _dom_classes…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# helpers_05_08可在附录中找到\n",
    "import helpers_05_08\n",
    "helpers_05_08.plot_tree_interactive(X, y);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Notice that as the depth increases, we tend to get very strangely shaped classification regions; for example, at a depth of five, there is a tall and skinny purple region between the yellow and blue regions.\n",
    "It's clear that this is less a result of the true, intrinsic data distribution, and more a result of the particular sampling or noise properties of the data.\n",
    "That is, this decision tree, even at only five levels deep, is clearly over-fitting our data.\n",
    "\n",
    "随着深度（树节点层次）增加，我们会得到一个非常奇怪的分类区域形状；如上面深度为5时，图像下部会出现一条很高的狭长紫色区域，处于绿色和蓝色区域之间。从直觉上我们就可以认为这是错误的，这个结果不是来源自数据的内在分布特性，而更像是通过数据中个别的样本或噪音获得的。也就是说决策树即使只有5层深度也发生了数据的过拟合。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision trees and over-fitting\n",
    "\n",
    "### 决策树和过拟合\n",
    "\n",
    "> Such over-fitting turns out to be a general property of decision trees: it is very easy to go too deep in the tree, and thus to fit details of the particular data rather than the overall properties of the distributions they are drawn from.\n",
    "Another way to see this over-fitting is to look at models trained on different subsets of the data—for example, in this figure we train two different trees, each on half of the original data:\n",
    "\n",
    "这种过拟合是决策树经常出现的问题：很可能就构建了一个深度太大的决策树，这样的树会聚焦在数据的特定数据点或噪音之上，而不是数据本身的分布特性之上。另外一种看出过拟合的方法是在数据不同子集上的训练结果，例如，下面两张图表示的是在数据集各一半的数据电商训练得到的两个不同的模型："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](figures/05.08-decision-tree-overfitting.png)\n",
    "[附录中生成图像的代码](06.00-Figure-Code.ipynb#Decision-Tree-Overfitting)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> It is clear that in some places, the two trees produce consistent results (e.g., in the four corners), while in other places, the two trees give very different classifications (e.g., in the regions between any two clusters).\n",
    "The key observation is that the inconsistencies tend to happen where the classification is less certain, and thus by using information from *both* of these trees, we might come up with a better result!\n",
    "\n",
    "很明显在一些位置上，两棵树都产生了相同的结果（例如四个角附近的位置），但是在其他位置上，两个模型给出了非常差异的分类结果（例如在两个分类的交界处）。这里的关键是这些差异一般会出现在分类器确定性较低的位置，因此如果我们同时使用这两棵树的特性的话，可以得到更好的结果。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> If you are running this notebook live, the following function will allow you to interactively display the fits of trees trained on a random subset of the data:\n",
    "\n",
    "如果使用notebooke交互模式，下面的函数能动态展示使用数据的随机子集训练得到的模型："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a627c4dd425a4588b22da94480a0d83d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='random_state', options=(0, 100), value=0), Output()), _dom_classes…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# helpers_05_08代码能在附录中找到\n",
    "import helpers_05_08\n",
    "helpers_05_08.randomized_tree_interactive(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Just as using information from two trees improves our results, we might expect that using information from many trees would improve our results even further.\n",
    "\n",
    "正如使用两棵树的信息能改善结果，我们可以认为如果使用更多的树的信息能够更好的改善结果。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ensembles of Estimators: Random Forests\n",
    "\n",
    "## 评估器合成：随机森林\n",
    "\n",
    "> This notion—that multiple overfitting estimators can be combined to reduce the effect of this overfitting—is what underlies an ensemble method called *bagging*.\n",
    "Bagging makes use of an ensemble (a grab bag, perhaps) of parallel estimators, each of which over-fits the data, and averages the results to find a better classification.\n",
    "An ensemble of randomized decision trees is known as a *random forest*.\n",
    "\n",
    "前面的术语，即多个过拟合的评估器可以被合并来减少过拟合的方法，被称为*装袋*，是一种团体学习的算法。装袋将一些并行的评估器组装（类似塞到袋子里）起来，其中的每个评估器都会产生过拟合，然后对结果求平均来得到一个更好的分类。对随机决策树的组装被称为*随机森林*。\n",
    "\n",
    "> This type of bagging classification can be done manually using Scikit-Learn's ``BaggingClassifier`` meta-estimator, as shown here:\n",
    "\n",
    "这种类型的装袋分类可以通过Scikit-Learn的`BagginClassifier`元评估器来手动实现，如下例："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "\n",
    "tree = DecisionTreeClassifier()\n",
    "bag = BaggingClassifier(tree, n_estimators=100, max_samples=0.8,\n",
    "                        random_state=1)\n",
    "\n",
    "bag.fit(X, y)\n",
    "visualize_classifier(bag, X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> In this example, we have randomized the data by fitting each estimator with a random subset of 80% of the training points.\n",
    "In practice, decision trees are more effectively randomized by injecting some stochasticity in how the splits are chosen: this way all the data contributes to the fit each time, but the results of the fit still have the desired randomness.\n",
    "For example, when determining which feature to split on, the randomized tree might select from among the top several features.\n",
    "You can read more technical details about these randomization strategies in the [Scikit-Learn documentation](http://scikit-learn.org/stable/modules/ensemble.html#forest) and references within.\n",
    "\n",
    "在上例中，我们通过在数据集的随机80%的数据点上拟合出100个模型。在实践中，决策树可以通过注入更多的随机性来选择子数据集以达到更好的效果：这个方法中所有的数据在每次拟合过程中都会产生贡献，但是拟合的结果仍然具有期望的随机性。例如当决定哪个特征来划分数据集时，随机决策树可以从前面几个特征中进行不同的选择。你可以在[Scikit-Learn在线文档](http://scikit-learn.org/stable/modules/ensemble.html#forest)中督导更多这些随机策略的技术细节。\n",
    "\n",
    "> In Scikit-Learn, such an optimized ensemble of randomized decision trees is implemented in the ``RandomForestClassifier`` estimator, which takes care of all the randomization automatically.\n",
    "All you need to do is select a number of estimators, and it will very quickly (in parallel, if desired) fit the ensemble of trees:\n",
    "\n",
    "在Scikit-Learn中，上述的随机决策树的优化组合算法被实现在`RandomForestClassifier`评估器中，它能全自动地处理所有的随机情况。你只需要设置评估器的个数，它能迅速的（如果需要的话会进行并行计算）拟合整个森林："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "model = RandomForestClassifier(n_estimators=100, random_state=0)\n",
    "visualize_classifier(model, X, y);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> We see that by averaging over 100 randomly perturbed models, we end up with an overall model that is much closer to our intuition about how the parameter space should be split.\n",
    "\n",
    "上面例子可以看到，通过在100个随机选择的模型上进行平均，我们能够得到一个更加复合我们对数据集分布的直觉模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Regression\n",
    "\n",
    "## 随机森林回归\n",
    "\n",
    "> In the previous section we considered random forests within the context of classification.\n",
    "Random forests can also be made to work in the case of regression (that is, continuous rather than categorical variables). The estimator to use for this is the ``RandomForestRegressor``, and the syntax is very similar to what we saw earlier.\n",
    "\n",
    "在前面内容中我们介绍了随机森林应用在分类场景下的方法。随机森林也能在回归场景中使用（也就是说不是处理离散的分类而是处理连续的分类）。实现这个场景的评估器是`RandomForestRegressor`，它的语法和前面看到的分类语法很相似。\n",
    "\n",
    "> Consider the following data, drawn from the combination of a fast and slow oscillation:\n",
    "\n",
    "考虑下面由一个快速震荡和缓慢震荡组合得到的数据集："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rng = np.random.RandomState(42)\n",
    "x = 10 * rng.rand(200)\n",
    "\n",
    "def model(x, sigma=0.3):\n",
    "    fast_oscillation = np.sin(5 * x)\n",
    "    slow_oscillation = np.sin(0.5 * x)\n",
    "    noise = sigma * rng.randn(len(x))\n",
    "\n",
    "    return slow_oscillation + fast_oscillation + noise\n",
    "\n",
    "y = model(x)\n",
    "plt.errorbar(x, y, 0.3, fmt='o');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Using the random forest regressor, we can find the best fit curve as follows:\n",
    "\n",
    "使用随机森林回归，我们能获得下面的最佳拟合曲线："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "forest = RandomForestRegressor(200)\n",
    "forest.fit(x[:, None], y)\n",
    "\n",
    "xfit = np.linspace(0, 10, 1000)\n",
    "yfit = forest.predict(xfit[:, None])\n",
    "ytrue = model(xfit, sigma=0)\n",
    "\n",
    "plt.errorbar(x, y, 0.3, fmt='o', alpha=0.5)\n",
    "plt.plot(xfit, yfit, '-r');\n",
    "plt.plot(xfit, ytrue, '-k', alpha=0.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Here the true model is shown in the smooth gray curve, while the random forest model is shown by the jagged red curve.\n",
    "As you can see, the non-parametric random forest model is flexible enough to fit the multi-period data, without us needing to specifying a multi-period model!\n",
    "\n",
    "上面真实的数据使用灰色光滑的曲线展示，而随机森林模型使用红色锯齿曲线展示。可以看到无参数的随机森林模型可以足够灵活的拟合多周期数据，甚至不需要指定任何多周期模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example: Random Forest for Classifying Digits\n",
    "\n",
    "## 例子：使用随机森林分类手写数字\n",
    "\n",
    "> Earlier we took a quick look at the hand-written digits data (see [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb)).\n",
    "Let's use that again here to see how the random forest classifier can be used in this context.\n",
    "\n",
    "前面我们快速的浏览了一下手写数字数据（参见[Scikit-Learn简介](05.02-Introducing-Scikit-Learn.ipynb)）。下面我们来看看随机森林分类器在这个场景下的应用。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'target_names', 'images', 'DESCR'])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "digits = load_digits()\n",
    "digits.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> To remind us what we're looking at, we'll visualize the first few data points:\n",
    "\n",
    "展示前面若干数据点，方便我们理解面对的数据集情况："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 64 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 设置图表\n",
    "fig = plt.figure(figsize=(6, 6))  # 图表尺寸\n",
    "fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)\n",
    "\n",
    "# 绘制数字，每个数字都是8x8大小\n",
    "for i in range(64):\n",
    "    ax = fig.add_subplot(8, 8, i + 1, xticks=[], yticks=[])\n",
    "    ax.imshow(digits.images[i], cmap=plt.cm.binary, interpolation='nearest')\n",
    "    \n",
    "    # 添加数字的标签\n",
    "    ax.text(0, 7, str(digits.target[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> We can quickly classify the digits using a random forest as follows:\n",
    "\n",
    "然后我们很快就能使用随机森林来分类这些数字："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "Xtrain, Xtest, ytrain, ytest = train_test_split(digits.data, digits.target,\n",
    "                                                random_state=0)\n",
    "model = RandomForestClassifier(n_estimators=1000)\n",
    "model.fit(Xtrain, ytrain)\n",
    "ypred = model.predict(Xtest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> We can take a look at the classification report for this classifier:\n",
    "\n",
    "然后看一下这个分类器的分类性能报告："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.97      0.99        38\n",
      "           1       1.00      0.96      0.98        45\n",
      "           2       0.95      1.00      0.98        42\n",
      "           3       0.98      0.98      0.98        45\n",
      "           4       0.97      1.00      0.99        37\n",
      "           5       0.98      0.98      0.98        48\n",
      "           6       1.00      1.00      1.00        52\n",
      "           7       1.00      0.96      0.98        50\n",
      "           8       0.94      0.98      0.96        46\n",
      "           9       0.98      0.98      0.98        47\n",
      "\n",
      "    accuracy                           0.98       450\n",
      "   macro avg       0.98      0.98      0.98       450\n",
      "weighted avg       0.98      0.98      0.98       450\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(metrics.classification_report(ypred, ytest))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> And for good measure, plot the confusion matrix:\n",
    "\n",
    "为了更清晰，可以绘制混淆矩阵："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "mat = confusion_matrix(ytest, ypred)\n",
    "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False)\n",
    "plt.xlabel('true label')\n",
    "plt.ylabel('predicted label');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> We find that a simple, untuned random forest results in a very accurate classification of the digits data.\n",
    "\n",
    "从这个例子我们发现，一个这么简单未经过优化的随机森林就能在手写数字分类上得到非常精确的分类结果。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary of Random Forests\n",
    "\n",
    "## 随机森林总结\n",
    "\n",
    "> This section contained a brief introduction to the concept of *ensemble estimators*, and in particular the random forest – an ensemble of randomized decision trees.\n",
    "Random forests are a powerful method with several advantages:\n",
    "\n",
    "> - Both training and prediction are very fast, because of the simplicity of the underlying decision trees. In addition, both tasks can be straightforwardly parallelized, because the individual trees are entirely independent entities.\n",
    "- The multiple trees allow for a probabilistic classification: a majority vote among estimators gives an estimate of the probability (accessed in Scikit-Learn with the ``predict_proba()`` method).\n",
    "- The nonparametric model is extremely flexible, and can thus perform well on tasks that are under-fit by other estimators.\n",
    "\n",
    "本节中介绍了*组合评估器*的概念，进一步介绍了随机森林，一个随机化决策树的团队算法。随机森林由于下述优点使其成为很强大的一个方法：\n",
    "\n",
    "- 训练和预测都非常快，因为其基础决策树计算非常简单。并且所有的任务都能直接地并行化，因为每一棵独立的树都是完全无关的。\n",
    "- 多棵决策树都可以进行概率分类：区域内的大多数票给出了概率分布的估计值（使用Scikit-Learn的`predict_proba()`实现）。\n",
    "- 无参数的模型非常灵活，可以在其他评估器欠拟合的情况下工作得很良好。\n",
    "\n",
    "> A primary disadvantage of random forests is that the results are not easily interpretable: that is, if you would like to draw conclusions about the *meaning* of the classification model, random forests may not be the best choice.\n",
    "\n",
    "随机森林最主要的缺点在于结果不容易解释：也即是说，如果你试图从分类模型中提取深层次的*含义*的话，随机森林可能不是最好的选择。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) | [Contents](Index.ipynb) | [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb) >\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.08-Random-Forests.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
